def distance_hammingword_modular (fm_train_dna=traindna,fm_test_dna=testdna, fm_test_real=testdat,order=3,gap=0,reverse=False,use_sign=False): from modshogun import StringCharFeatures, StringWordFeatures, DNA from modshogun import SortWordString from modshogun import HammingWordDistance charfeat=StringCharFeatures(DNA) charfeat.set_features(fm_train_dna) feats_train=StringWordFeatures(charfeat.get_alphabet()) feats_train.obtain_from_char(charfeat, order-1, order, gap, reverse) preproc=SortWordString() preproc.init(feats_train) feats_train.add_preprocessor(preproc) feats_train.apply_preprocessor() charfeat=StringCharFeatures(DNA) charfeat.set_features(fm_test_dna) feats_test=StringWordFeatures(charfeat.get_alphabet()) feats_test.obtain_from_char(charfeat, order-1, order, gap, reverse) feats_test.add_preprocessor(preproc) feats_test.apply_preprocessor() distance=HammingWordDistance(feats_train, feats_train, use_sign) dm_train=distance.get_distance_matrix() distance.init(feats_train, feats_test) dm_test=distance.get_distance_matrix() return distance,dm_train,dm_test
def preprocessor_sortwordstring_modular(fm_train_dna=traindna, fm_test_dna=testdna, order=3, gap=0, reverse=False, use_sign=False): from modshogun import CommWordStringKernel from modshogun import StringCharFeatures, StringWordFeatures, DNA from modshogun import SortWordString charfeat = StringCharFeatures(fm_train_dna, DNA) feats_train = StringWordFeatures(charfeat.get_alphabet()) feats_train.obtain_from_char(charfeat, order - 1, order, gap, reverse) preproc = SortWordString() preproc.init(feats_train) feats_train.add_preprocessor(preproc) feats_train.apply_preprocessor() charfeat = StringCharFeatures(fm_test_dna, DNA) feats_test = StringWordFeatures(charfeat.get_alphabet()) feats_test.obtain_from_char(charfeat, order - 1, order, gap, reverse) feats_test.add_preprocessor(preproc) feats_test.apply_preprocessor() kernel = CommWordStringKernel(feats_train, feats_train, use_sign) km_train = kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test = kernel.get_kernel_matrix() return km_train, km_test, kernel
def distance_manhattenword_modular(train_fname=traindna, test_fname=testdna, order=3, gap=0, reverse=False): from modshogun import StringCharFeatures, StringWordFeatures, DNA from modshogun import SortWordString, ManhattanWordDistance, CSVFile charfeat = StringCharFeatures(CSVFile(train_fname), DNA) feats_train = StringWordFeatures(charfeat.get_alphabet()) feats_train.obtain_from_char(charfeat, order - 1, order, gap, reverse) preproc = SortWordString() preproc.init(feats_train) feats_train.add_preprocessor(preproc) feats_train.apply_preprocessor() charfeat = StringCharFeatures(CSVFile(test_fname), DNA) feats_test = StringWordFeatures(charfeat.get_alphabet()) feats_test.obtain_from_char(charfeat, order - 1, order, gap, reverse) feats_test.add_preprocessor(preproc) feats_test.apply_preprocessor() distance = ManhattanWordDistance(feats_train, feats_train) dm_train = distance.get_distance_matrix() distance.init(feats_train, feats_test) dm_test = distance.get_distance_matrix() return dm_train, dm_test
def kernel_comm_ulong_string_modular(fm_train_dna=traindat, fm_test_dna=testdat, order=3, gap=0, reverse=False): from modshogun import CommUlongStringKernel from modshogun import StringUlongFeatures, StringCharFeatures, DNA from modshogun import SortUlongString charfeat = StringCharFeatures(DNA) charfeat.set_features(fm_train_dna) feats_train = StringUlongFeatures(charfeat.get_alphabet()) feats_train.obtain_from_char(charfeat, order - 1, order, gap, reverse) preproc = SortUlongString() preproc.init(feats_train) feats_train.add_preprocessor(preproc) feats_train.apply_preprocessor() charfeat = StringCharFeatures(DNA) charfeat.set_features(fm_test_dna) feats_test = StringUlongFeatures(charfeat.get_alphabet()) feats_test.obtain_from_char(charfeat, order - 1, order, gap, reverse) feats_test.add_preprocessor(preproc) feats_test.apply_preprocessor() use_sign = False kernel = CommUlongStringKernel(feats_train, feats_train, use_sign) km_train = kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test = kernel.get_kernel_matrix() return km_train, km_test, kernel
def kernel_histogram_word_string_modular (fm_train_dna=traindat,fm_test_dna=testdat,label_train_dna=label_traindat,order=3,gap=0,reverse=False): from modshogun import StringCharFeatures, StringWordFeatures, DNA, BinaryLabels from modshogun import HistogramWordStringKernel from modshogun import PluginEstimate#, MSG_DEBUG reverse = reverse charfeat=StringCharFeatures(DNA) #charfeat.io.set_loglevel(MSG_DEBUG) charfeat.set_features(fm_train_dna) feats_train=StringWordFeatures(charfeat.get_alphabet()) feats_train.obtain_from_char(charfeat, order-1, order, gap, reverse) charfeat=StringCharFeatures(DNA) charfeat.set_features(fm_test_dna) feats_test=StringWordFeatures(charfeat.get_alphabet()) feats_test.obtain_from_char(charfeat, order-1, order, gap, reverse) pie=PluginEstimate() labels=BinaryLabels(label_train_dna) pie.set_labels(labels) pie.set_features(feats_train) pie.train() kernel=HistogramWordStringKernel(feats_train, feats_train, pie) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) pie.set_features(feats_test) pie.apply().get_labels() km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def kernel_comm_ulong_string_modular (fm_train_dna=traindat,fm_test_dna=testdat, order=3, gap=0, reverse = False): from modshogun import CommUlongStringKernel from modshogun import StringUlongFeatures, StringCharFeatures, DNA from modshogun import SortUlongString charfeat=StringCharFeatures(DNA) charfeat.set_features(fm_train_dna) feats_train=StringUlongFeatures(charfeat.get_alphabet()) feats_train.obtain_from_char(charfeat, order-1, order, gap, reverse) preproc=SortUlongString() preproc.init(feats_train) feats_train.add_preprocessor(preproc) feats_train.apply_preprocessor() charfeat=StringCharFeatures(DNA) charfeat.set_features(fm_test_dna) feats_test=StringUlongFeatures(charfeat.get_alphabet()) feats_test.obtain_from_char(charfeat, order-1, order, gap, reverse) feats_test.add_preprocessor(preproc) feats_test.apply_preprocessor() use_sign=False kernel=CommUlongStringKernel(feats_train, feats_train, use_sign) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def distance_canberraword_modular (fm_train_dna=traindna,fm_test_dna=testdna,order=3,gap=0,reverse=False): from modshogun import StringCharFeatures, StringWordFeatures, DNA from modshogun import SortWordString from modshogun import CanberraWordDistance charfeat=StringCharFeatures(DNA) charfeat.set_features(fm_train_dna) feats_train=StringWordFeatures(charfeat.get_alphabet()) feats_train.obtain_from_char(charfeat, order-1, order, gap, reverse) preproc=SortWordString() preproc.init(feats_train) feats_train.add_preprocessor(preproc) feats_train.apply_preprocessor() charfeat=StringCharFeatures(DNA) charfeat.set_features(fm_test_dna) feats_test=StringWordFeatures(charfeat.get_alphabet()) feats_test.obtain_from_char(charfeat, order-1, order, gap, reverse) feats_test.add_preprocessor(preproc) feats_test.apply_preprocessor() distance=CanberraWordDistance(feats_train, feats_train) dm_train=distance.get_distance_matrix() distance.init(feats_train, feats_test) dm_test=distance.get_distance_matrix() return distance,dm_train,dm_test
def kernel_salzberg_word_string_modular(fm_train_dna=traindat, fm_test_dna=testdat, label_train_dna=label_traindat, order=3, gap=0, reverse=False): from modshogun import StringCharFeatures, StringWordFeatures, DNA, BinaryLabels from modshogun import SalzbergWordStringKernel from modshogun import PluginEstimate charfeat = StringCharFeatures(fm_train_dna, DNA) feats_train = StringWordFeatures(charfeat.get_alphabet()) feats_train.obtain_from_char(charfeat, order - 1, order, gap, reverse) charfeat = StringCharFeatures(fm_test_dna, DNA) feats_test = StringWordFeatures(charfeat.get_alphabet()) feats_test.obtain_from_char(charfeat, order - 1, order, gap, reverse) pie = PluginEstimate() labels = BinaryLabels(label_train_dna) pie.set_labels(labels) pie.set_features(feats_train) pie.train() kernel = SalzbergWordStringKernel(feats_train, feats_train, pie, labels) km_train = kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) pie.set_features(feats_test) pie.apply().get_labels() km_test = kernel.get_kernel_matrix() return km_train, km_test, kernel
def kernel_histogram_word_string_modular (fm_train_dna=traindat,fm_test_dna=testdat,label_train_dna=label_traindat,order=3,ppseudo_count=1,npseudo_count=1): from modshogun import StringCharFeatures, StringWordFeatures, DNA, BinaryLabels from modshogun import HistogramWordStringKernel, AvgDiagKernelNormalizer from modshogun import PluginEstimate#, MSG_DEBUG charfeat=StringCharFeatures(DNA) #charfeat.io.set_loglevel(MSG_DEBUG) charfeat.set_features(fm_train_dna) feats_train=StringWordFeatures(charfeat.get_alphabet()) feats_train.obtain_from_char(charfeat, order-1, order, 0, False) charfeat=StringCharFeatures(DNA) charfeat.set_features(fm_test_dna) feats_test=StringWordFeatures(charfeat.get_alphabet()) feats_test.obtain_from_char(charfeat, order-1, order, 0, False) pie=PluginEstimate(ppseudo_count,npseudo_count) labels=BinaryLabels(label_train_dna) pie.set_labels(labels) pie.set_features(feats_train) pie.train() kernel=HistogramWordStringKernel(feats_train, feats_train, pie) km_train=kernel.get_kernel_matrix() kernel.init(feats_train, feats_test) pie.set_features(feats_test) pie.apply().get_labels() km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def kernel_fisher_modular(fm_train_dna=traindat, fm_test_dna=testdat, label_train_dna=label_traindat, N=1, M=4, pseudo=1e-1, order=1, gap=0, reverse=False, kargs=[1, False, True]): from modshogun import StringCharFeatures, StringWordFeatures, FKFeatures, DNA from modshogun import PolyKernel from modshogun import HMM, BW_NORMAL #, MSG_DEBUG # train HMM for positive class charfeat = StringCharFeatures(fm_hmm_pos, DNA) #charfeat.io.set_loglevel(MSG_DEBUG) hmm_pos_train = StringWordFeatures(charfeat.get_alphabet()) hmm_pos_train.obtain_from_char(charfeat, order - 1, order, gap, reverse) pos = HMM(hmm_pos_train, N, M, pseudo) pos.baum_welch_viterbi_train(BW_NORMAL) # train HMM for negative class charfeat = StringCharFeatures(fm_hmm_neg, DNA) hmm_neg_train = StringWordFeatures(charfeat.get_alphabet()) hmm_neg_train.obtain_from_char(charfeat, order - 1, order, gap, reverse) neg = HMM(hmm_neg_train, N, M, pseudo) neg.baum_welch_viterbi_train(BW_NORMAL) # Kernel training data charfeat = StringCharFeatures(fm_train_dna, DNA) wordfeats_train = StringWordFeatures(charfeat.get_alphabet()) wordfeats_train.obtain_from_char(charfeat, order - 1, order, gap, reverse) # Kernel testing data charfeat = StringCharFeatures(fm_test_dna, DNA) wordfeats_test = StringWordFeatures(charfeat.get_alphabet()) wordfeats_test.obtain_from_char(charfeat, order - 1, order, gap, reverse) # get kernel on training data pos.set_observations(wordfeats_train) neg.set_observations(wordfeats_train) feats_train = FKFeatures(10, pos, neg) feats_train.set_opt_a(-1) #estimate prior kernel = PolyKernel(feats_train, feats_train, *kargs) km_train = kernel.get_kernel_matrix() # get kernel on testing data pos_clone = HMM(pos) neg_clone = HMM(neg) pos_clone.set_observations(wordfeats_test) neg_clone.set_observations(wordfeats_test) feats_test = FKFeatures(10, pos_clone, neg_clone) feats_test.set_a(feats_train.get_a()) #use prior from training data kernel.init(feats_train, feats_test) km_test = kernel.get_kernel_matrix() return km_train, km_test, kernel
def kernel_top_modular(fm_train_dna=traindat, fm_test_dna=testdat, label_train_dna=label_traindat, pseudo=1e-1, order=1, gap=0, reverse=False, kargs=[1, False, True]): from modshogun import StringCharFeatures, StringWordFeatures, TOPFeatures, DNA from modshogun import PolyKernel from modshogun import HMM, BW_NORMAL N = 1 # toy HMM with 1 state M = 4 # 4 observations -> DNA # train HMM for positive class charfeat = StringCharFeatures(fm_hmm_pos, DNA) hmm_pos_train = StringWordFeatures(charfeat.get_alphabet()) hmm_pos_train.obtain_from_char(charfeat, order - 1, order, gap, reverse) pos = HMM(hmm_pos_train, N, M, pseudo) pos.baum_welch_viterbi_train(BW_NORMAL) # train HMM for negative class charfeat = StringCharFeatures(fm_hmm_neg, DNA) hmm_neg_train = StringWordFeatures(charfeat.get_alphabet()) hmm_neg_train.obtain_from_char(charfeat, order - 1, order, gap, reverse) neg = HMM(hmm_neg_train, N, M, pseudo) neg.baum_welch_viterbi_train(BW_NORMAL) # Kernel training data charfeat = StringCharFeatures(fm_train_dna, DNA) wordfeats_train = StringWordFeatures(charfeat.get_alphabet()) wordfeats_train.obtain_from_char(charfeat, order - 1, order, gap, reverse) # Kernel testing data charfeat = StringCharFeatures(fm_test_dna, DNA) wordfeats_test = StringWordFeatures(charfeat.get_alphabet()) wordfeats_test.obtain_from_char(charfeat, order - 1, order, gap, reverse) # get kernel on training data pos.set_observations(wordfeats_train) neg.set_observations(wordfeats_train) feats_train = TOPFeatures(10, pos, neg, False, False) kernel = PolyKernel(feats_train, feats_train, *kargs) km_train = kernel.get_kernel_matrix() # get kernel on testing data pos_clone = HMM(pos) neg_clone = HMM(neg) pos_clone.set_observations(wordfeats_test) neg_clone.set_observations(wordfeats_test) feats_test = TOPFeatures(10, pos_clone, neg_clone, False, False) kernel.init(feats_train, feats_test) km_test = kernel.get_kernel_matrix() return km_train, km_test, kernel
def get_kernel_mat(fm_train_dna, fm_test_dna, N, M, pseudo=1e-1, order=1, gap=0, reverse=False): # train HMM for positive class print "hmm training" charfeat = StringCharFeatures(fm_train_dna, DNA) #charfeat.io.set_loglevel(MSG_DEBUG) hmm_train = StringWordFeatures(charfeat.get_alphabet()) hmm_train.obtain_from_char(charfeat, order - 1, order, gap, reverse) pos = HMM(hmm_train, N, M, pseudo) pos.baum_welch_viterbi_train(BW_NORMAL) neg = HMM(pos) print "Kernel training data" charfeat = StringCharFeatures(fm_train_dna, DNA) wordfeats_train = StringWordFeatures(charfeat.get_alphabet()) wordfeats_train.obtain_from_char(charfeat, order - 1, order, gap, reverse) print "Kernel testing data" charfeat = StringCharFeatures(fm_test_dna, DNA) wordfeats_test = StringWordFeatures(charfeat.get_alphabet()) wordfeats_test.obtain_from_char(charfeat, order - 1, order, gap, reverse) print "get kernel on training data" pos.set_observations(wordfeats_train) neg.set_observations(wordfeats_train) feats_train = FKFeatures(10, pos, neg) feats_train.set_opt_a(-1) #estimate prior print 'getting feature matrix' v0 = feats_train.get_feature_vector(0) v1 = feats_train.get_feature_vector(1) print np.dot(v0, v1) kernel = LinearKernel(feats_train, feats_train) #kernel=PolyKernel(feats_train, feats_train, *kargs) km_train = kernel.get_kernel_matrix() print km_train.shape, km_train[0, 1] print "get kernel on testing data" pos_clone = HMM(pos) neg_clone = HMM(neg) pos_clone.set_observations(wordfeats_test) neg_clone.set_observations(wordfeats_test) feats_test = FKFeatures(10, pos_clone, neg_clone) feats_test.set_a(feats_train.get_a()) #use prior from training data kernel.init(feats_train, feats_test) km_test = kernel.get_kernel_matrix() return km_train, km_test, kernel
def kernel_fisher_modular (fm_train_dna=traindat, fm_test_dna=testdat, label_train_dna=label_traindat, N=1,M=4,pseudo=1e-1,order=1,gap=0,reverse=False, kargs=[1,False,True]): from modshogun import StringCharFeatures, StringWordFeatures, FKFeatures, DNA from modshogun import PolyKernel from modshogun import HMM, BW_NORMAL#, MSG_DEBUG # train HMM for positive class charfeat=StringCharFeatures(fm_hmm_pos, DNA) #charfeat.io.set_loglevel(MSG_DEBUG) hmm_pos_train=StringWordFeatures(charfeat.get_alphabet()) hmm_pos_train.obtain_from_char(charfeat, order-1, order, gap, reverse) pos=HMM(hmm_pos_train, N, M, pseudo) pos.baum_welch_viterbi_train(BW_NORMAL) # train HMM for negative class charfeat=StringCharFeatures(fm_hmm_neg, DNA) hmm_neg_train=StringWordFeatures(charfeat.get_alphabet()) hmm_neg_train.obtain_from_char(charfeat, order-1, order, gap, reverse) neg=HMM(hmm_neg_train, N, M, pseudo) neg.baum_welch_viterbi_train(BW_NORMAL) # Kernel training data charfeat=StringCharFeatures(fm_train_dna, DNA) wordfeats_train=StringWordFeatures(charfeat.get_alphabet()) wordfeats_train.obtain_from_char(charfeat, order-1, order, gap, reverse) # Kernel testing data charfeat=StringCharFeatures(fm_test_dna, DNA) wordfeats_test=StringWordFeatures(charfeat.get_alphabet()) wordfeats_test.obtain_from_char(charfeat, order-1, order, gap, reverse) # get kernel on training data pos.set_observations(wordfeats_train) neg.set_observations(wordfeats_train) feats_train=FKFeatures(10, pos, neg) feats_train.set_opt_a(-1) #estimate prior kernel=PolyKernel(feats_train, feats_train, *kargs) km_train=kernel.get_kernel_matrix() # get kernel on testing data pos_clone=HMM(pos) neg_clone=HMM(neg) pos_clone.set_observations(wordfeats_test) neg_clone.set_observations(wordfeats_test) feats_test=FKFeatures(10, pos_clone, neg_clone) feats_test.set_a(feats_train.get_a()) #use prior from training data kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def kernel_top_modular (fm_train_dna=traindat,fm_test_dna=testdat,label_train_dna=label_traindat,pseudo=1e-1, order=1,gap=0,reverse=False,kargs=[1, False, True]): from modshogun import StringCharFeatures, StringWordFeatures, TOPFeatures, DNA from modshogun import PolyKernel from modshogun import HMM, BW_NORMAL N=1 # toy HMM with 1 state M=4 # 4 observations -> DNA # train HMM for positive class charfeat=StringCharFeatures(fm_hmm_pos, DNA) hmm_pos_train=StringWordFeatures(charfeat.get_alphabet()) hmm_pos_train.obtain_from_char(charfeat, order-1, order, gap, reverse) pos=HMM(hmm_pos_train, N, M, pseudo) pos.baum_welch_viterbi_train(BW_NORMAL) # train HMM for negative class charfeat=StringCharFeatures(fm_hmm_neg, DNA) hmm_neg_train=StringWordFeatures(charfeat.get_alphabet()) hmm_neg_train.obtain_from_char(charfeat, order-1, order, gap, reverse) neg=HMM(hmm_neg_train, N, M, pseudo) neg.baum_welch_viterbi_train(BW_NORMAL) # Kernel training data charfeat=StringCharFeatures(fm_train_dna, DNA) wordfeats_train=StringWordFeatures(charfeat.get_alphabet()) wordfeats_train.obtain_from_char(charfeat, order-1, order, gap, reverse) # Kernel testing data charfeat=StringCharFeatures(fm_test_dna, DNA) wordfeats_test=StringWordFeatures(charfeat.get_alphabet()) wordfeats_test.obtain_from_char(charfeat, order-1, order, gap, reverse) # get kernel on training data pos.set_observations(wordfeats_train) neg.set_observations(wordfeats_train) feats_train=TOPFeatures(10, pos, neg, False, False) kernel=PolyKernel(feats_train, feats_train, *kargs) km_train=kernel.get_kernel_matrix() # get kernel on testing data pos_clone=HMM(pos) neg_clone=HMM(neg) pos_clone.set_observations(wordfeats_test) neg_clone.set_observations(wordfeats_test) feats_test=TOPFeatures(10, pos_clone, neg_clone, False, False) kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def get_kernel_mat(fm_train_dna, fm_test_dna, N, M, pseudo=1e-1,order=1,gap=0,reverse=False): # train HMM for positive class print "hmm training" charfeat=StringCharFeatures(fm_train_dna, DNA) #charfeat.io.set_loglevel(MSG_DEBUG) hmm_train=StringWordFeatures(charfeat.get_alphabet()) hmm_train.obtain_from_char(charfeat, order-1, order, gap, reverse) pos=HMM(hmm_train, N, M, pseudo) pos.baum_welch_viterbi_train(BW_NORMAL) neg = HMM(pos) print "Kernel training data" charfeat=StringCharFeatures(fm_train_dna, DNA) wordfeats_train=StringWordFeatures(charfeat.get_alphabet()) wordfeats_train.obtain_from_char(charfeat, order-1, order, gap, reverse) print "Kernel testing data" charfeat=StringCharFeatures(fm_test_dna, DNA) wordfeats_test=StringWordFeatures(charfeat.get_alphabet()) wordfeats_test.obtain_from_char(charfeat, order-1, order, gap, reverse) print "get kernel on training data" pos.set_observations(wordfeats_train) neg.set_observations(wordfeats_train) feats_train=FKFeatures(10, pos, neg) feats_train.set_opt_a(-1) #estimate prior print 'getting feature matrix' v0 = feats_train.get_feature_vector(0) v1 = feats_train.get_feature_vector(1) print np.dot(v0, v1) kernel=LinearKernel(feats_train, feats_train) #kernel=PolyKernel(feats_train, feats_train, *kargs) km_train=kernel.get_kernel_matrix() print km_train.shape, km_train[0, 1] print "get kernel on testing data" pos_clone=HMM(pos) neg_clone=HMM(neg) pos_clone.set_observations(wordfeats_test) neg_clone.set_observations(wordfeats_test) feats_test=FKFeatures(10, pos_clone, neg_clone) feats_test.set_a(feats_train.get_a()) #use prior from training data kernel.init(feats_train, feats_test) km_test=kernel.get_kernel_matrix() return km_train,km_test,kernel
def get_feature_mat(fm_train_dna, fm_test_dna, N, M, pseudo=1e-1,order=1,gap=0,reverse=False): # train HMM for positive class print "hmm training" charfeat=StringCharFeatures(fm_train_dna, DNA) #charfeat.io.set_loglevel(MSG_DEBUG) hmm_train=StringWordFeatures(charfeat.get_alphabet()) hmm_train.obtain_from_char(charfeat, order-1, order, gap, reverse) pos=HMM(hmm_train, N, M, pseudo) pos.baum_welch_viterbi_train(BW_NORMAL) neg = HMM(pos) print "Kernel training data" charfeat=StringCharFeatures(fm_train_dna, DNA) wordfeats_train=StringWordFeatures(charfeat.get_alphabet()) wordfeats_train.obtain_from_char(charfeat, order-1, order, gap, reverse) print "Kernel testing data" charfeat=StringCharFeatures(fm_test_dna, DNA) wordfeats_test=StringWordFeatures(charfeat.get_alphabet()) wordfeats_test.obtain_from_char(charfeat, order-1, order, gap, reverse) print "get kernel on training data" pos.set_observations(wordfeats_train) neg.set_observations(wordfeats_train) feats_train=FKFeatures(10, pos, neg) feats_train.set_opt_a(-1) #estimate prior print 'getting feature train' train_featmat = [] for i in range(len(fm_train_dna)): train_featmat.append(feats_train.get_computed_dot_feature_vector(i)) train_featmat = np.array(train_featmat) print "get feature on testing" pos_clone=HMM(pos) neg_clone=HMM(neg) pos_clone.set_observations(wordfeats_test) neg_clone.set_observations(wordfeats_test) feats_test=FKFeatures(10, pos_clone, neg_clone) feats_test.set_a(feats_train.get_a()) #use prior from training data test_featmat = [] for i in range(len(fm_test_dna)): test_featmat.append(feats_test.get_feature_vector(i)) test_featmat = np.array(test_featmat) return train_featmat, test_featmat
def distribution_linearhmm_modular (fm_dna=traindna,order=3,gap=0,reverse=False): from modshogun import StringWordFeatures, StringCharFeatures, DNA from modshogun import LinearHMM charfeat=StringCharFeatures(DNA) charfeat.set_features(fm_dna) feats=StringWordFeatures(charfeat.get_alphabet()) feats.obtain_from_char(charfeat, order-1, order, gap, reverse) hmm=LinearHMM(feats) hmm.train() hmm.get_transition_probs() num_examples=feats.get_num_vectors() num_param=hmm.get_num_model_parameters() for i in range(num_examples): for j in range(num_param): hmm.get_log_derivative(j, i) out_likelihood = hmm.get_log_likelihood() out_sample = hmm.get_log_likelihood_sample() return hmm,out_likelihood ,out_sample
def distribution_linearhmm_modular(fm_dna=traindna, order=3, gap=0, reverse=False): from modshogun import StringWordFeatures, StringCharFeatures, DNA from modshogun import LinearHMM charfeat = StringCharFeatures(DNA) charfeat.set_features(fm_dna) feats = StringWordFeatures(charfeat.get_alphabet()) feats.obtain_from_char(charfeat, order - 1, order, gap, reverse) hmm = LinearHMM(feats) hmm.train() hmm.get_transition_probs() num_examples = feats.get_num_vectors() num_param = hmm.get_num_model_parameters() for i in range(num_examples): for j in range(num_param): hmm.get_log_derivative(j, i) out_likelihood = hmm.get_log_likelihood() out_sample = hmm.get_log_likelihood_sample() return hmm, out_likelihood, out_sample
def distribution_hmm_modular(fm_cube, N, M, pseudo, order, gap, reverse, num_examples): from modshogun import StringWordFeatures, StringCharFeatures, CUBE from modshogun import HMM, BW_NORMAL charfeat=StringCharFeatures(CUBE) charfeat.set_features(fm_cube) feats=StringWordFeatures(charfeat.get_alphabet()) feats.obtain_from_char(charfeat, order-1, order, gap, reverse) hmm=HMM(feats, N, M, pseudo) hmm.train() hmm.baum_welch_viterbi_train(BW_NORMAL) num_examples=feats.get_num_vectors() num_param=hmm.get_num_model_parameters() for i in range(num_examples): for j in range(num_param): hmm.get_log_derivative(j, i) best_path=0 best_path_state=0 for i in range(num_examples): best_path+=hmm.best_path(i) for j in range(N): best_path_state+=hmm.get_best_path_state(i, j) lik_example = hmm.get_log_likelihood() lik_sample = hmm.get_log_likelihood_sample() return lik_example, lik_sample, hmm
def get_spectrum_features(data, order=3, gap=0, reverse=True): """ create feature object used by spectrum kernel """ charfeat = StringCharFeatures(data, DNA) feat = StringWordFeatures(charfeat.get_alphabet()) feat.obtain_from_char(charfeat, order-1, order, gap, reverse) preproc = SortWordString() preproc.init(feat) feat.add_preprocessor(preproc) feat.apply_preprocessor() return feat
def get_spectrum_features(data, order=3, gap=0, reverse=True): """ create feature object used by spectrum kernel """ charfeat = StringCharFeatures(data, PROTEIN) feat = StringWordFeatures(charfeat.get_alphabet()) feat.obtain_from_char(charfeat, order - 1, order, gap, reverse) preproc = SortWordString() preproc.init(feat) feat.add_preprocessor(preproc) feat.apply_preprocessor() return feat
def distance_manhattenword_modular (train_fname=traindna,test_fname=testdna,order=3,gap=0,reverse=False): from modshogun import StringCharFeatures, StringWordFeatures, DNA from modshogun import SortWordString, ManhattanWordDistance, CSVFile charfeat=StringCharFeatures(CSVFile(train_fname), DNA) feats_train=StringWordFeatures(charfeat.get_alphabet()) feats_train.obtain_from_char(charfeat, order-1, order, gap, reverse) preproc=SortWordString() preproc.init(feats_train) feats_train.add_preprocessor(preproc) feats_train.apply_preprocessor() charfeat=StringCharFeatures(CSVFile(test_fname), DNA) feats_test=StringWordFeatures(charfeat.get_alphabet()) feats_test.obtain_from_char(charfeat, order-1, order, gap, reverse) feats_test.add_preprocessor(preproc) feats_test.apply_preprocessor() distance=ManhattanWordDistance(feats_train, feats_train) dm_train=distance.get_distance_matrix() distance.init(feats_train, feats_test) dm_test=distance.get_distance_matrix() return dm_train,dm_test
def make_string_feature (astringv, start=1, order=8, gap=0, reverse=False): from modshogun import StringUlongFeatures, StringCharFeatures, RAWBYTE from modshogun import SortUlongString charfeat=StringCharFeatures(astringv, RAWBYTE) feats_train=StringUlongFeatures(charfeat.get_alphabet()) feats_train.obtain_from_char(charfeat, start, order, gap, reverse) preproc=SortUlongString() preproc.init(feats_train) feats_train.add_preprocessor(preproc) feats_train.apply_preprocessor() return feats_train
def distribution_ppwm_modular(fm_dna=traindna, order=3): from modshogun import StringByteFeatures, StringCharFeatures, DNA from modshogun import PositionalPWM from numpy import array, e, log, exp charfeat = StringCharFeatures(DNA) charfeat.set_features(fm_dna) feats = StringByteFeatures(charfeat.get_alphabet()) feats.obtain_from_char(charfeat, order - 1, order, 0, False) L = 20 k = 3 sigma = 1 mu = 4 ppwm = PositionalPWM() ppwm.set_sigma(sigma) ppwm.set_mean(mu) pwm = array([[0.0, 0.5, 0.1, 1.0], [0.0, 0.5, 0.5, 0.0], [1.0, 0.0, 0.4, 0.0], [0.0, 0.0, 0.0, 0.0]]) pwm = array([[0.01, 0.09, 0.1], [0.09, 0.01, 0.1], [0.85, 0.4, 0.1], [0.05, 0.5, 0.7]]) ppwm.set_pwm(log(pwm)) #print(ppwm.get_pwm()) ppwm.compute_w(L) w = ppwm.get_w() #print(w) #from pylab import * #figure(1) #pcolor(exp(w)) #pcolor(w) #colorbar() #figure(2) ppwm.compute_scoring(1) u = ppwm.get_scoring(0) #pcolor(exp(u)) #show() #ppwm=PositionalPWM(feats) #ppwm.train() #out_likelihood = histo.get_log_likelihood() #out_sample = histo.get_log_likelihood_sample() return w, u
def distribution_ppwm_modular(fm_dna=traindna, order=3): from modshogun import StringByteFeatures, StringCharFeatures, DNA from modshogun import PositionalPWM from numpy import array, e, log, exp charfeat = StringCharFeatures(DNA) charfeat.set_features(fm_dna) feats = StringByteFeatures(charfeat.get_alphabet()) feats.obtain_from_char(charfeat, order - 1, order, 0, False) L = 20 k = 3 sigma = 1 mu = 4 ppwm = PositionalPWM() ppwm.set_sigma(sigma) ppwm.set_mean(mu) pwm = array([[0.0, 0.5, 0.1, 1.0], [0.0, 0.5, 0.5, 0.0], [1.0, 0.0, 0.4, 0.0], [0.0, 0.0, 0.0, 0.0]]) pwm = array([[0.01, 0.09, 0.1], [0.09, 0.01, 0.1], [0.85, 0.4, 0.1], [0.05, 0.5, 0.7]]) ppwm.set_pwm(log(pwm)) # print(ppwm.get_pwm()) ppwm.compute_w(L) w = ppwm.get_w() # print(w) # from pylab import * # figure(1) # pcolor(exp(w)) # pcolor(w) # colorbar() # figure(2) ppwm.compute_scoring(1) u = ppwm.get_scoring(0) # pcolor(exp(u)) # show() # ppwm=PositionalPWM(feats) # ppwm.train() # out_likelihood = histo.get_log_likelihood() # out_sample = histo.get_log_likelihood_sample() return w, u
def distribution_histogram_modular (fm_dna=traindna,order=3,gap=0,reverse=False): from modshogun import StringWordFeatures, StringCharFeatures, DNA from modshogun import Histogram charfeat=StringCharFeatures(DNA) charfeat.set_features(fm_dna) feats=StringWordFeatures(charfeat.get_alphabet()) feats.obtain_from_char(charfeat, order-1, order, gap, reverse) histo=Histogram(feats) histo.train() histo.get_histogram() num_examples=feats.get_num_vectors() num_param=histo.get_num_model_parameters() #for i in xrange(num_examples): # for j in xrange(num_param): # histo.get_log_derivative(j, i) out_likelihood = histo.get_log_likelihood() out_sample = histo.get_log_likelihood_sample() return histo,out_sample,out_likelihood
def distribution_histogram_modular(fm_dna=traindna, order=3, gap=0, reverse=False): from modshogun import StringWordFeatures, StringCharFeatures, DNA from modshogun import Histogram charfeat = StringCharFeatures(DNA) charfeat.set_features(fm_dna) feats = StringWordFeatures(charfeat.get_alphabet()) feats.obtain_from_char(charfeat, order - 1, order, gap, reverse) histo = Histogram(feats) histo.train() histo.get_histogram() num_examples = feats.get_num_vectors() num_param = histo.get_num_model_parameters() # for i in xrange(num_examples): # for j in xrange(num_param): # histo.get_log_derivative(j, i) out_likelihood = histo.get_log_likelihood() out_sample = histo.get_log_likelihood_sample() return histo, out_sample, out_likelihood
def tests_check_commwordkernel_memleak_modular (num, order, gap, reverse): import gc from modshogun import Alphabet,StringCharFeatures,StringWordFeatures,DNA from modshogun import SortWordString, MSG_DEBUG from modshogun import CommWordStringKernel, IdentityKernelNormalizer from numpy import mat POS=[num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT'] NEG=[num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'TTGT', num*'TTGT', num*'TTGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT',num*'ACGT', num*'ACGT', num*'ACGT'] for i in range(10): alpha=Alphabet(DNA) traindat=StringCharFeatures(alpha) traindat.set_features(POS+NEG) trainudat=StringWordFeatures(traindat.get_alphabet()); trainudat.obtain_from_char(traindat, order-1, order, gap, reverse) #trainudat.io.set_loglevel(MSG_DEBUG) pre = SortWordString() #pre.io.set_loglevel(MSG_DEBUG) pre.init(trainudat) trainudat.add_preprocessor(pre) trainudat.apply_preprocessor() spec = CommWordStringKernel(10, False) spec.set_normalizer(IdentityKernelNormalizer()) spec.init(trainudat, trainudat) K=spec.get_kernel_matrix() del POS del NEG del order del gap del reverse return K
def tests_check_commwordkernel_memleak_modular(num, order, gap, reverse): import gc from modshogun import Alphabet, StringCharFeatures, StringWordFeatures, DNA from modshogun import SortWordString, MSG_DEBUG from modshogun import CommWordStringKernel, IdentityKernelNormalizer from numpy import mat POS = [ num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT' ] NEG = [ num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'TTGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT', num * 'ACGT' ] for i in range(10): alpha = Alphabet(DNA) traindat = StringCharFeatures(alpha) traindat.set_features(POS + NEG) trainudat = StringWordFeatures(traindat.get_alphabet()) trainudat.obtain_from_char(traindat, order - 1, order, gap, reverse) #trainudat.io.set_loglevel(MSG_DEBUG) pre = SortWordString() #pre.io.set_loglevel(MSG_DEBUG) pre.init(trainudat) trainudat.add_preprocessor(pre) trainudat.apply_preprocessor() spec = CommWordStringKernel(10, False) spec.set_normalizer(IdentityKernelNormalizer()) spec.init(trainudat, trainudat) K = spec.get_kernel_matrix() del POS del NEG del order del gap del reverse return K